Traces ↗

Documentation Index#

Fetch the complete documentation index at: https://docs.crewai.com/llms.txt Use this file to discover all available pages before exploring further.

Using Traces to monitor your Crews

Overview#

Traces provide comprehensive visibility into your crew executions, helping you monitor performance, debug issues, and optimize your AI agent workflows.

What are Traces?#

Traces in CrewAI AMP are detailed execution records that capture every aspect of your crew’s operation, from initial inputs to final outputs. They record:

• Agent thoughts and reasoning
• Task execution details
• Tool usage and outputs
• Token consumption metrics
• Execution times
• Cost estimates

Accessing Traces#

Once in your CrewAI AMP dashboard, click on the Traces to view all execution records.

You’ll see a list of all crew executions, sorted by date. Click on any execution to view its detailed trace.

Understanding the Trace Interface#

The trace interface is divided into several sections, each providing different insights into your crew’s execution:

1. Execution Summary#

The top section displays high-level metrics about the execution:

• Total Tokens: Number of tokens consumed across all tasks
• Prompt Tokens: Tokens used in prompts to the LLM
• Completion Tokens: Tokens generated in LLM responses
• Requests: Number of API calls made
• Execution Time: Total duration of the crew run
• Estimated Cost: Approximate cost based on token usage

2. Tasks & Agents#

This section shows all tasks and agents that were part of the crew execution:

• Task name and agent assignment
• Agents and LLMs used for each task
• Status (completed/failed)
• Individual execution time of the task

3. Final Output#

Displays the final result produced by the crew after all tasks are completed.

4. Execution Timeline#

A visual representation of when each task started and ended, helping you identify bottlenecks or parallel execution patterns.

5. Detailed Task View#

When you click on a specific task in the timeline or task list, you’ll see:

• Task Key: Unique identifier for the task
• Task ID: Technical identifier in the system
• Status: Current state (completed/running/failed)
• Agent: Which agent performed the task
• LLM: Language model used for this task
• Start/End Time: When the task began and completed
• Execution Time: Duration of this specific task
• Task Description: What the agent was instructed to do
• Expected Output: What output format was requested
• Input: Any input provided to this task from previous tasks
• Output: The actual result produced by the agent

Using Traces for Debugging#

Traces are invaluable for troubleshooting issues with your crews:

When a crew execution doesn’t produce the expected results, examine the trace to find where things went wrong. Look for:

• Failed tasks

• Unexpected agent decisions

• Tool usage errors

• Misinterpreted instructions

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  Use execution metrics to identify performance bottlenecks:

• Tasks that took longer than expected

• Excessive token usage

• Redundant tool operations

• Unnecessary API calls

  Analyze token usage and cost estimates to optimize your crew’s efficiency:

• Consider using smaller models for simpler tasks

• Refine prompts to be more concise

• Cache frequently accessed information

• Structure tasks to minimize redundant operations

Performance and batching#

CrewAI batches trace uploads to reduce overhead on high-volume runs:

• A TraceBatchManager buffers events and sends them in batches via the Plus API client
• Reduces network chatter and improves reliability on flaky connections
• Automatically enabled in the default trace listener; no configuration needed

This yields more stable tracing under load while preserving detailed task/agent telemetry.

Contact our support team for assistance with trace analysis or any other CrewAI AMP features.